alexander mikov - program tools for dynamic investigation of social networks
TRANSCRIPT
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Authors:Alexander Mikov, Elena Zamyatina,Daria Germanova
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Cuban State University, Cuban State University, Higher School Of Economics (Perm Branch)Higher School Of Economics (Perm Branch),,Perm State UniverityPerm State Univerity
Program Tools for Dynamic Investigation of Social Networks
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Higher School Of EconomicsNational Research University
Perm Branch
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Outline
• BackGround• Related Works• Specific Properties of Simulation Software • Simulation Model Representation• The Algorithm of Investigation• Graphs
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Background
• Social Network Analyses:– Telecommunication.– Marketing.– Sociology.– Etc.
• Information
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Related Works
• NetLogo• Repast (Recursive Porous Agent
Simulation Toolkit)• Mason
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Winter Simulation Conference • Jonathan K. Alt, Stephen Lieberman. Representing
Dynamic Social Networks In Discrete Event Social Simulation (Modeling, Virtual Environments and Simulation (MOVES) Institute, Naval Postgraduate School, Monterey, California 93943, USA
• Gatti et al. A Simulation-based Approach To Analyze The Information Diffusion In Microblogging Online Social Network. IBM Research-Brazil Av. Tut´oia 1157, San Paulo (SP), BRAZIL
• O¨ zgu¨r O¨ zmen et al. An Agent-based Simulation Study Of A Complex Adaptive Collaboration Network. Oak Ridge National Laboratory. Oak Ridge, TN 37830, USA
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Requirements:
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Simulation system Triad and it’s purposes in past
• Triad – 80-90 years of 20-th century• Linguistic and software tools for computer
aided design of computer systems (aviation industry)
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Triad.Net – the Distributed Simulation System with a remote access
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TriadClientTriadBuilder,TriaDebuggerTriadSecurity
Web-browser
TriadEditor,TriadBuilder,
TriadDebuggerTriadSecurity
Data Base
Server
Server
Simulation Model
TriadBalance
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Triad.Net components
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TriadEditor-the subsystem for Triad-model design
TriadDebugger – Triad verification and validation
TriadMining – the subsystem of an output data intellectual processing
TriadSecurity – the safety subsystem
TriadCompile-Triad-compiler
TriadBuilder-the subsystem of completeness of partly defined model
TriadCore-the Kernel of Triad simulation system
TriadBalance – the subsystem with load balancing function
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Simulation model description
• Structure layer (STR)
• Routine layerRoutine layer ( (ROUTROUT))• Message layer (MES)Message layer (MES)
• The layer of structure is dedicated to the description of the objects of the simulation model and their interconnections
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Simulation model description
• Structure layerStructure layer ((STRSTR))..
• Routine layer (ROUT).
• Message layerMessage layer ((MESMES))..
• The layer of routines describes the behavior of the simulation model objects
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Simulation model description
• Structure layerStructure layer ((STRSTR))..
• Routine layerRoutine layer ((ROUTROUT))..
• Message layer (MES).
• The layer of messages is used for description
of the messages with complicated structures
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Structure layer operations
• To add/to delete the poles, nodes and arcs.• The operations with graphs (union, intersection and so on)• Graph constants using.
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P[2]
P[1]
P[2]
P[1]
V[1]V[2]
G
G = G + V[2](A)
V[2]
A
+ (V[2].A -> V[1].P[1])open V[1] := Str1 [ P[1]=Outp, P[2]=Inp ]
Outp
Inp
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Graph Constants
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Simple path
Path(m)
dPath(m)
Simple cycle
Cycle(m)
dCycle(m)
Complete
Compl(m)
dCompl(m)
Grid
Rectan(m1,m2…)
dRectan(m1,m2…)
Tree
Tree(m,n)
dTree(m,n)
Bipart
Bipart(m,n)
dBipart(m,n)
Star
Star(m)
dStar(m)
Disconnected
nc(m)
dnc(m)
m=4m=6m=4m1=2; m2=3m=3; n=2m=2; n=3m=5m=5
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Random graph (model of Erdösh-Renyi) with 30 nodes and probability p=0,25.
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Random graph (model of Erdösh-Renyi) with a number of nodes=30 and a probability p=1.
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Barabashi-Albert graph
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An example: the model of computer network and
it’s description using Triad
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Router
Router
Router
Router
Router
Type Router,Host; integer i;M:=dStar(Rout[5]<Pol[4]>);M:=M+node Hst[8]<Pol>;M.Rout[0]=>Router;for i:=1 by 1 to 4 do
M.Rout[i]=>Router;M:=M+edge(Rout[i].Pol[1]—
Hst[2*i-2]);M:=M+edge(Rout[i].Pol[2]—
Hst[2*i-1]);endf;for i:=0 by 1 to 7 do
M.Hst[i]=>Host;endf;
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Graphical Editor
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The Algorithm of Investigation
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Min(var)
Count(event)
Check(Pol)
Information procedureSimulation Model
Conditions of simulation
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A List of Standard Information Procedures in Graphical Editor
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An example of information procedure –intellectual analyze
information procedure EVENT_SEQUENCE (in ref event E1,E2,E3;out Boolean ARRIVED)
initial interlock (E2,E3); ARRIVED := false; case of E1:available(E2); E2:available( E3); E3:ARRIVED:=true; endc endinf
24An investigator may detect the arrival of the sequence of events E1→E2→E3
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Structural Characteritics
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Conditions of simulation
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Conditions of simulation<name>(<a list of generic parameters>)
(<input and output formal parameters>) initial <a sequence of statements> endi
<a list of information procedures> <a sequence of statements>
processing <a sequence of statements>…endprocendcond
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Simulation run
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simulatesimulate
<<a list of an elements of models, being inspected> on conditions of simulation <name>
(a list of actual generic parameters>)[<a list of input and output actual parameters>]
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Distributed simulation model
• Distributed simulation models – a set of logical processes being fulfilled on different compute nodes and communicating with one another by passing messages;
• Each Logical process has local time calendar
• Time paradox – arriving input message with time stamp less than local time
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N1
N4
N3
N2
N6N5
N1
N4
N3
N2
N6N5
LP1
LP2
Distributed Models
30LP1 LP2 LPN
Time Time paradox
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Two classes of algorithms
• Conservative
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Optimizations - lookahead
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Optimistic Algorithm
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Problems: •Rollbacks•Memory for states
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To find out the hidden dependences of events
Simulation model structure
The sequence of accumulated events
The results of the former runs
The expert knowledge
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Expirements
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ILLIAC
050
100150200250300350400
0 20 40 60 80
количество вычислительных узлов
врем
я вы
полн
ения
Оптимистический АлгоритмКонсервативный алгоритмАлгоритм TriadRule
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Experiments
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Откаты
020406080
100120140160
0 20 40 60 80
количество узлов
коли
чест
во о
ткат
ов
Оптимистический алгоритм
Алгоритм TriadRule
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Thank you for your kindly Attention
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Load balancing
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The reasonses of disbalance
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Communication lines geterogenity
Computing nodes geterogenity
Distributed application geterogenity
Simulation Model Operations
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Node1(Head Node)
Node4(Compute Node)
Node2(Compute Node)
Node5(Compute Node)
Node3(Compute Node)
Node6(Compute Node)
Object 1 Object 2
Object 3 Object 4
Object 8 Object 9
Object 6
Object 10
Object 7
Object 5
Node1(Head Node)
Node4(Compute Node)
Node2(Compute Node)
Node5(Compute Node)
Node3(Compute Node)
Node6(Compute Node)
Object 1 Object 2
Object 3 Object 4
Object 8Object 9
Object 6
Object 10
Object 7
Object 5
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Decentralized algorithm multiagent load balancing subsystem
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Compute node
Cluster
Data baseRemote Access
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Computing Node and a Simulation Model Fragment
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Monitoring Agent
(SM)
Monitoring Agent
(CS)
Agent of Analyses
Agent of distribution
Agent of migration
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Communication of the agents
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Agent of migrationAgent of distribution
Black Board Black Board
Monitoring Agent Monitoring Agent
Agent of AnalysesAgent of
distribution
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Rules and metarules for the agent of distribution
MetaRule
MetaRule1 MetaRule2 MetaRuleN
1Rule 2Rule 3Rule RuleK 1+RuleK
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The procedure for the frequency of specific event monitoring
infprocedure EventAverage(event V; in real T ) : realinitial
integer eventCount;real time;eventCount := 0;time := 0;
endihandling
time := T;eventCount:= eventCount + 1;
endhprocessing
EventAverage := eventCount / time;endp
endinf
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Subsystem of visualization of the results of monitoring
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Conclusion
• Multimodel investigation of computer networks
• Adaptability of software to incorporate into a simulation model new devices and new algorithms that govern their work
• Simulation Model Completeness Analyses and Simulation model Redefining
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Conclusions
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Thank you for your kindly Attention
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